{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v7Cycmf3Zbok"
      },
      "source": [
        "# Stock NeurIPS2018 Part 3. Backtest\n",
        "This series is a reproduction of paper *the process in the paper Practical Deep Reinforcement Learning Approach for Stock Trading*. \n",
        "\n",
        "This is the third and last part of the NeurIPS2018 series, introducing how to use use the agents we trained to do backtest, and compare with baselines such as Mean Variance Optimization and DJIA index.\n",
        "\n",
        "Other demos can be found at the repo of [FinRL-Tutorials]((https://github.com/AI4Finance-Foundation/FinRL-Tutorials))."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1oWbj4HgqHBg"
      },
      "source": [
        "# Part 1. Install Packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "QJgoEYx3p_NG"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: swig in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (4.1.1)\n",
            "Requirement already satisfied: wrds in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (3.1.6)\n",
            "Requirement already satisfied: numpy in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from wrds) (1.26.0)\n",
            "Requirement already satisfied: pandas in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from wrds) (2.1.1)\n",
            "Requirement already satisfied: psycopg2-binary in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from wrds) (2.9.9)\n",
            "Requirement already satisfied: scipy in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from wrds) (1.11.3)\n",
            "Requirement already satisfied: sqlalchemy<2 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from wrds) (1.4.49)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from pandas->wrds) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from pandas->wrds) (2023.3.post1)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from pandas->wrds) (2023.3)\n",
            "Requirement already satisfied: six>=1.5 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas->wrds) (1.16.0)\n",
            "Requirement already satisfied: pyportfolioopt in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (1.5.5)\n",
            "Requirement already satisfied: cvxpy<2.0.0,>=1.1.19 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from pyportfolioopt) (1.4.1)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.22.4 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from pyportfolioopt) (1.26.0)\n",
            "Requirement already satisfied: pandas>=0.19 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from pyportfolioopt) (2.1.1)\n",
            "Requirement already satisfied: scipy<2.0,>=1.3 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from pyportfolioopt) (1.11.3)\n",
            "Requirement already satisfied: osqp>=0.6.2 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from cvxpy<2.0.0,>=1.1.19->pyportfolioopt) (0.6.3)\n",
            "Requirement already satisfied: ecos>=2 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from cvxpy<2.0.0,>=1.1.19->pyportfolioopt) (2.0.12)\n",
            "Requirement already satisfied: clarabel>=0.5.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from cvxpy<2.0.0,>=1.1.19->pyportfolioopt) (0.6.0)\n",
            "Requirement already satisfied: scs>=3.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from cvxpy<2.0.0,>=1.1.19->pyportfolioopt) (3.2.3)\n",
            "Requirement already satisfied: pybind11 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from cvxpy<2.0.0,>=1.1.19->pyportfolioopt) (2.11.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from pandas>=0.19->pyportfolioopt) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from pandas>=0.19->pyportfolioopt) (2023.3.post1)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from pandas>=0.19->pyportfolioopt) (2023.3)\n",
            "Requirement already satisfied: qdldl in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from osqp>=0.6.2->cvxpy<2.0.0,>=1.1.19->pyportfolioopt) (0.1.7.post0)\n",
            "Requirement already satisfied: six>=1.5 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas>=0.19->pyportfolioopt) (1.16.0)\n",
            "Collecting git+https://github.com/AI4Finance-Foundation/FinRL.git\n",
            "  Cloning https://github.com/AI4Finance-Foundation/FinRL.git to /private/var/folders/pc/8l9dz1f949ddzztd4yfcytx80000gn/T/pip-req-build-6hrmrp8b\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/AI4Finance-Foundation/FinRL.git /private/var/folders/pc/8l9dz1f949ddzztd4yfcytx80000gn/T/pip-req-build-6hrmrp8b\n",
            "  Resolved https://github.com/AI4Finance-Foundation/FinRL.git to commit 7c71056dd6d72e205096696319a2d8bd4a2bfe23\n",
            "  Installing build dependencies ... \u001b[?25ldone\n",
            "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
            "\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
            "\u001b[?25hCollecting elegantrl@ git+https://github.com/AI4Finance-Foundation/ElegantRL.git#egg=elegantrl (from finrl==0.3.6)\n",
            "  Cloning https://github.com/AI4Finance-Foundation/ElegantRL.git to /private/var/folders/pc/8l9dz1f949ddzztd4yfcytx80000gn/T/pip-install-qjr44cy9/elegantrl_55204e69217544c5b59af518abeda00f\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/AI4Finance-Foundation/ElegantRL.git /private/var/folders/pc/8l9dz1f949ddzztd4yfcytx80000gn/T/pip-install-qjr44cy9/elegantrl_55204e69217544c5b59af518abeda00f\n",
            "  Resolved https://github.com/AI4Finance-Foundation/ElegantRL.git to commit a68515548417093006eb7f68738b55a3a758645e\n",
            "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25hRequirement already satisfied: alpaca-trade-api<4,>=3 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from finrl==0.3.6) (3.0.2)\n",
            "Requirement already satisfied: ccxt<4,>=3 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from finrl==0.3.6) (3.1.60)\n",
            "Requirement already satisfied: exchange-calendars<5,>=4 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from finrl==0.3.6) (4.5)\n",
            "Requirement already satisfied: jqdatasdk<2,>=1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from finrl==0.3.6) (1.9.1)\n",
            "Requirement already satisfied: pyfolio<0.10,>=0.9 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from finrl==0.3.6) (0.9.2)\n",
            "Requirement already satisfied: pyportfolioopt<2,>=1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from finrl==0.3.6) (1.5.5)\n",
            "Requirement already satisfied: ray[default,tune]<3,>=2 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from finrl==0.3.6) (2.7.1)\n",
            "Requirement already satisfied: scikit-learn<2,>=1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from finrl==0.3.6) (1.3.1)\n",
            "Requirement already satisfied: stable-baselines3[extra]>=2.0.0a5 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from finrl==0.3.6) (2.1.0)\n",
            "Requirement already satisfied: stockstats<0.6,>=0.5 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from finrl==0.3.6) (0.5.4)\n",
            "Requirement already satisfied: wrds<4,>=3 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from finrl==0.3.6) (3.1.6)\n",
            "Requirement already satisfied: yfinance<0.3,>=0.2 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from finrl==0.3.6) (0.2.31)\n",
            "Requirement already satisfied: pandas>=0.18.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from alpaca-trade-api<4,>=3->finrl==0.3.6) (2.1.1)\n",
            "Requirement already satisfied: numpy>=1.11.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from alpaca-trade-api<4,>=3->finrl==0.3.6) (1.26.0)\n",
            "Requirement already satisfied: requests<3,>2 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from alpaca-trade-api<4,>=3->finrl==0.3.6) (2.31.0)\n",
            "Requirement already satisfied: urllib3<2,>1.24 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from alpaca-trade-api<4,>=3->finrl==0.3.6) (1.26.17)\n",
            "Requirement already satisfied: websocket-client<2,>=0.56.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from alpaca-trade-api<4,>=3->finrl==0.3.6) (1.6.4)\n",
            "Requirement already satisfied: websockets<11,>=9.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from alpaca-trade-api<4,>=3->finrl==0.3.6) (10.4)\n",
            "Requirement already satisfied: msgpack==1.0.3 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from alpaca-trade-api<4,>=3->finrl==0.3.6) (1.0.3)\n",
            "Requirement already satisfied: aiohttp==3.8.2 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from alpaca-trade-api<4,>=3->finrl==0.3.6) (3.8.2)\n",
            "Requirement already satisfied: PyYAML==6.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from alpaca-trade-api<4,>=3->finrl==0.3.6) (6.0)\n",
            "Requirement already satisfied: deprecation==2.1.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from alpaca-trade-api<4,>=3->finrl==0.3.6) (2.1.0)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from aiohttp==3.8.2->alpaca-trade-api<4,>=3->finrl==0.3.6) (23.1.0)\n",
            "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from aiohttp==3.8.2->alpaca-trade-api<4,>=3->finrl==0.3.6) (2.1.1)\n",
            "Requirement already satisfied: multidict<6.0,>=4.5 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from aiohttp==3.8.2->alpaca-trade-api<4,>=3->finrl==0.3.6) (5.2.0)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from aiohttp==3.8.2->alpaca-trade-api<4,>=3->finrl==0.3.6) (4.0.3)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from aiohttp==3.8.2->alpaca-trade-api<4,>=3->finrl==0.3.6) (1.9.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from aiohttp==3.8.2->alpaca-trade-api<4,>=3->finrl==0.3.6) (1.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from aiohttp==3.8.2->alpaca-trade-api<4,>=3->finrl==0.3.6) (1.3.1)\n",
            "Requirement already satisfied: packaging in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from deprecation==2.1.0->alpaca-trade-api<4,>=3->finrl==0.3.6) (23.2)\n",
            "Requirement already satisfied: setuptools>=60.9.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ccxt<4,>=3->finrl==0.3.6) (68.0.0)\n",
            "Requirement already satisfied: certifi>=2018.1.18 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ccxt<4,>=3->finrl==0.3.6) (2023.7.22)\n",
            "Requirement already satisfied: cryptography>=2.6.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ccxt<4,>=3->finrl==0.3.6) (41.0.4)\n",
            "Requirement already satisfied: aiodns>=1.1.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ccxt<4,>=3->finrl==0.3.6) (3.1.0)\n",
            "Requirement already satisfied: pyluach in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from exchange-calendars<5,>=4->finrl==0.3.6) (2.2.0)\n",
            "Requirement already satisfied: python-dateutil in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from exchange-calendars<5,>=4->finrl==0.3.6) (2.8.2)\n",
            "Requirement already satisfied: toolz in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from exchange-calendars<5,>=4->finrl==0.3.6) (0.12.0)\n",
            "Requirement already satisfied: tzdata in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from exchange-calendars<5,>=4->finrl==0.3.6) (2023.3)\n",
            "Requirement already satisfied: korean-lunar-calendar in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from exchange-calendars<5,>=4->finrl==0.3.6) (0.3.1)\n",
            "Requirement already satisfied: six in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from jqdatasdk<2,>=1->finrl==0.3.6) (1.16.0)\n",
            "Requirement already satisfied: SQLAlchemy>=1.2.8 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from jqdatasdk<2,>=1->finrl==0.3.6) (1.4.49)\n",
            "Requirement already satisfied: thriftpy2>=0.3.9 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from jqdatasdk<2,>=1->finrl==0.3.6) (0.4.17)\n",
            "Requirement already satisfied: pymysql>=0.7.6 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from jqdatasdk<2,>=1->finrl==0.3.6) (1.1.0)\n",
            "Requirement already satisfied: ipython>=3.2.3 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from pyfolio<0.10,>=0.9->finrl==0.3.6) (8.16.1)\n",
            "Requirement already satisfied: matplotlib>=1.4.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from pyfolio<0.10,>=0.9->finrl==0.3.6) (3.8.0)\n",
            "Requirement already satisfied: pytz>=2014.10 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from pyfolio<0.10,>=0.9->finrl==0.3.6) (2023.3.post1)\n",
            "Requirement already satisfied: scipy>=0.14.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from pyfolio<0.10,>=0.9->finrl==0.3.6) (1.11.3)\n",
            "Requirement already satisfied: seaborn>=0.7.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from pyfolio<0.10,>=0.9->finrl==0.3.6) (0.13.0)\n",
            "Requirement already satisfied: empyrical>=0.5.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from pyfolio<0.10,>=0.9->finrl==0.3.6) (0.5.5)\n",
            "Requirement already satisfied: cvxpy<2.0.0,>=1.1.19 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from pyportfolioopt<2,>=1->finrl==0.3.6) (1.4.1)\n",
            "Requirement already satisfied: click>=7.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ray[default,tune]<3,>=2->finrl==0.3.6) (8.1.7)\n",
            "Requirement already satisfied: filelock in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ray[default,tune]<3,>=2->finrl==0.3.6) (3.12.4)\n",
            "Requirement already satisfied: jsonschema in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ray[default,tune]<3,>=2->finrl==0.3.6) (4.19.1)\n",
            "Requirement already satisfied: protobuf!=3.19.5,>=3.15.3 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ray[default,tune]<3,>=2->finrl==0.3.6) (4.24.4)\n",
            "Requirement already satisfied: tensorboardX>=1.9 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ray[default,tune]<3,>=2->finrl==0.3.6) (2.6.2.2)\n",
            "Requirement already satisfied: pyarrow>=6.0.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ray[default,tune]<3,>=2->finrl==0.3.6) (13.0.0)\n",
            "Requirement already satisfied: fsspec in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ray[default,tune]<3,>=2->finrl==0.3.6) (2023.9.2)\n",
            "Requirement already satisfied: aiohttp-cors in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ray[default,tune]<3,>=2->finrl==0.3.6) (0.7.0)\n",
            "Requirement already satisfied: colorful in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ray[default,tune]<3,>=2->finrl==0.3.6) (0.5.5)\n",
            "Requirement already satisfied: py-spy>=0.2.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ray[default,tune]<3,>=2->finrl==0.3.6) (0.3.14)\n",
            "Requirement already satisfied: gpustat>=1.0.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ray[default,tune]<3,>=2->finrl==0.3.6) (1.1.1)\n",
            "Requirement already satisfied: opencensus in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ray[default,tune]<3,>=2->finrl==0.3.6) (0.11.3)\n",
            "Requirement already satisfied: pydantic<2 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ray[default,tune]<3,>=2->finrl==0.3.6) (1.10.13)\n",
            "Requirement already satisfied: prometheus-client>=0.7.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ray[default,tune]<3,>=2->finrl==0.3.6) (0.17.1)\n",
            "Requirement already satisfied: smart-open in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ray[default,tune]<3,>=2->finrl==0.3.6) (6.4.0)\n",
            "Requirement already satisfied: virtualenv<20.21.1,>=20.0.24 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ray[default,tune]<3,>=2->finrl==0.3.6) (20.21.0)\n",
            "Requirement already satisfied: grpcio>=1.42.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ray[default,tune]<3,>=2->finrl==0.3.6) (1.59.0)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from scikit-learn<2,>=1->finrl==0.3.6) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from scikit-learn<2,>=1->finrl==0.3.6) (3.2.0)\n",
            "Requirement already satisfied: gymnasium<0.30,>=0.28.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (0.29.1)\n",
            "Requirement already satisfied: torch>=1.13 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (2.1.0)\n",
            "Requirement already satisfied: cloudpickle in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (2.2.1)\n",
            "Requirement already satisfied: opencv-python in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (4.8.1.78)\n",
            "Requirement already satisfied: pygame in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (2.1.0)\n",
            "Requirement already satisfied: tensorboard>=2.9.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (2.14.1)\n",
            "Requirement already satisfied: psutil in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (5.9.0)\n",
            "Requirement already satisfied: tqdm in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (4.66.1)\n",
            "Requirement already satisfied: rich in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (13.6.0)\n",
            "Requirement already satisfied: shimmy[atari]~=1.1.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (1.1.0)\n",
            "Requirement already satisfied: pillow in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (10.0.1)\n",
            "Requirement already satisfied: autorom[accept-rom-license]~=0.6.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (0.6.1)\n",
            "Requirement already satisfied: psycopg2-binary in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from wrds<4,>=3->finrl==0.3.6) (2.9.9)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from yfinance<0.3,>=0.2->finrl==0.3.6) (0.0.11)\n",
            "Requirement already satisfied: lxml>=4.9.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from yfinance<0.3,>=0.2->finrl==0.3.6) (4.9.3)\n",
            "Requirement already satisfied: appdirs>=1.4.4 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from yfinance<0.3,>=0.2->finrl==0.3.6) (1.4.4)\n",
            "Requirement already satisfied: frozendict>=2.3.4 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from yfinance<0.3,>=0.2->finrl==0.3.6) (2.3.8)\n",
            "Requirement already satisfied: peewee>=3.16.2 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from yfinance<0.3,>=0.2->finrl==0.3.6) (3.17.0)\n",
            "Requirement already satisfied: beautifulsoup4>=4.11.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from yfinance<0.3,>=0.2->finrl==0.3.6) (4.12.2)\n",
            "Requirement already satisfied: html5lib>=1.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from yfinance<0.3,>=0.2->finrl==0.3.6) (1.1)\n",
            "Requirement already satisfied: gym in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from elegantrl@ git+https://github.com/AI4Finance-Foundation/ElegantRL.git#egg=elegantrl->finrl==0.3.6) (0.26.2)\n",
            "Requirement already satisfied: pycares>=4.0.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from aiodns>=1.1.1->ccxt<4,>=3->finrl==0.3.6) (4.4.0)\n",
            "Requirement already satisfied: AutoROM.accept-rom-license in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from autorom[accept-rom-license]~=0.6.1->stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (0.6.1)\n",
            "Requirement already satisfied: soupsieve>1.2 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from beautifulsoup4>=4.11.1->yfinance<0.3,>=0.2->finrl==0.3.6) (2.5)\n",
            "Requirement already satisfied: cffi>=1.12 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from cryptography>=2.6.1->ccxt<4,>=3->finrl==0.3.6) (1.16.0)\n",
            "Requirement already satisfied: osqp>=0.6.2 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from cvxpy<2.0.0,>=1.1.19->pyportfolioopt<2,>=1->finrl==0.3.6) (0.6.3)\n",
            "Requirement already satisfied: ecos>=2 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from cvxpy<2.0.0,>=1.1.19->pyportfolioopt<2,>=1->finrl==0.3.6) (2.0.12)\n",
            "Requirement already satisfied: clarabel>=0.5.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from cvxpy<2.0.0,>=1.1.19->pyportfolioopt<2,>=1->finrl==0.3.6) (0.6.0)\n",
            "Requirement already satisfied: scs>=3.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from cvxpy<2.0.0,>=1.1.19->pyportfolioopt<2,>=1->finrl==0.3.6) (3.2.3)\n",
            "Requirement already satisfied: pybind11 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from cvxpy<2.0.0,>=1.1.19->pyportfolioopt<2,>=1->finrl==0.3.6) (2.11.1)\n",
            "Requirement already satisfied: pandas-datareader>=0.2 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from empyrical>=0.5.0->pyfolio<0.10,>=0.9->finrl==0.3.6) (0.10.0)\n",
            "Requirement already satisfied: nvidia-ml-py>=11.450.129 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from gpustat>=1.0.0->ray[default,tune]<3,>=2->finrl==0.3.6) (12.535.108)\n",
            "Requirement already satisfied: blessed>=1.17.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from gpustat>=1.0.0->ray[default,tune]<3,>=2->finrl==0.3.6) (1.20.0)\n",
            "Requirement already satisfied: typing-extensions>=4.3.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from gymnasium<0.30,>=0.28.1->stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (4.8.0)\n",
            "Requirement already satisfied: farama-notifications>=0.0.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from gymnasium<0.30,>=0.28.1->stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (0.0.4)\n",
            "Requirement already satisfied: webencodings in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from html5lib>=1.1->yfinance<0.3,>=0.2->finrl==0.3.6) (0.5.1)\n",
            "Requirement already satisfied: backcall in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ipython>=3.2.3->pyfolio<0.10,>=0.9->finrl==0.3.6) (0.2.0)\n",
            "Requirement already satisfied: decorator in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ipython>=3.2.3->pyfolio<0.10,>=0.9->finrl==0.3.6) (5.1.1)\n",
            "Requirement already satisfied: jedi>=0.16 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ipython>=3.2.3->pyfolio<0.10,>=0.9->finrl==0.3.6) (0.19.1)\n",
            "Requirement already satisfied: matplotlib-inline in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ipython>=3.2.3->pyfolio<0.10,>=0.9->finrl==0.3.6) (0.1.6)\n",
            "Requirement already satisfied: pickleshare in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ipython>=3.2.3->pyfolio<0.10,>=0.9->finrl==0.3.6) (0.7.5)\n",
            "Requirement already satisfied: prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ipython>=3.2.3->pyfolio<0.10,>=0.9->finrl==0.3.6) (3.0.39)\n",
            "Requirement already satisfied: pygments>=2.4.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ipython>=3.2.3->pyfolio<0.10,>=0.9->finrl==0.3.6) (2.16.1)\n",
            "Requirement already satisfied: stack-data in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ipython>=3.2.3->pyfolio<0.10,>=0.9->finrl==0.3.6) (0.6.2)\n",
            "Requirement already satisfied: traitlets>=5 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ipython>=3.2.3->pyfolio<0.10,>=0.9->finrl==0.3.6) (5.11.2)\n",
            "Requirement already satisfied: exceptiongroup in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ipython>=3.2.3->pyfolio<0.10,>=0.9->finrl==0.3.6) (1.1.3)\n",
            "Requirement already satisfied: pexpect>4.3 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ipython>=3.2.3->pyfolio<0.10,>=0.9->finrl==0.3.6) (4.8.0)\n",
            "Requirement already satisfied: appnope in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ipython>=3.2.3->pyfolio<0.10,>=0.9->finrl==0.3.6) (0.1.3)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from matplotlib>=1.4.0->pyfolio<0.10,>=0.9->finrl==0.3.6) (1.1.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from matplotlib>=1.4.0->pyfolio<0.10,>=0.9->finrl==0.3.6) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from matplotlib>=1.4.0->pyfolio<0.10,>=0.9->finrl==0.3.6) (4.43.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from matplotlib>=1.4.0->pyfolio<0.10,>=0.9->finrl==0.3.6) (1.4.5)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from matplotlib>=1.4.0->pyfolio<0.10,>=0.9->finrl==0.3.6) (3.1.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from requests<3,>2->alpaca-trade-api<4,>=3->finrl==0.3.6) (3.4)\n",
            "Requirement already satisfied: ale-py~=0.8.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from shimmy[atari]~=1.1.0->stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (0.8.1)\n",
            "Requirement already satisfied: absl-py>=0.4 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from tensorboard>=2.9.1->stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (2.0.0)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from tensorboard>=2.9.1->stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (2.23.3)\n",
            "Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from tensorboard>=2.9.1->stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (1.0.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from tensorboard>=2.9.1->stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (3.5)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from tensorboard>=2.9.1->stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (0.7.1)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from tensorboard>=2.9.1->stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (3.0.0)\n",
            "Requirement already satisfied: ply<4.0,>=3.4 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from thriftpy2>=0.3.9->jqdatasdk<2,>=1->finrl==0.3.6) (3.11)\n",
            "Requirement already satisfied: sympy in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from torch>=1.13->stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (1.12)\n",
            "Requirement already satisfied: networkx in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from torch>=1.13->stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (3.1)\n",
            "Requirement already satisfied: jinja2 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from torch>=1.13->stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (3.1.2)\n",
            "Requirement already satisfied: distlib<1,>=0.3.6 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from virtualenv<20.21.1,>=20.0.24->ray[default,tune]<3,>=2->finrl==0.3.6) (0.3.7)\n",
            "Requirement already satisfied: platformdirs<4,>=2.4 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from virtualenv<20.21.1,>=20.0.24->ray[default,tune]<3,>=2->finrl==0.3.6) (3.11.0)\n",
            "Requirement already satisfied: gym-notices>=0.0.4 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from gym->elegantrl@ git+https://github.com/AI4Finance-Foundation/ElegantRL.git#egg=elegantrl->finrl==0.3.6) (0.0.8)\n",
            "Requirement already satisfied: box2d-py==2.3.5 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from gym->elegantrl@ git+https://github.com/AI4Finance-Foundation/ElegantRL.git#egg=elegantrl->finrl==0.3.6) (2.3.5)\n",
            "Requirement already satisfied: swig==4.* in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from gym->elegantrl@ git+https://github.com/AI4Finance-Foundation/ElegantRL.git#egg=elegantrl->finrl==0.3.6) (4.1.1)\n",
            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from jsonschema->ray[default,tune]<3,>=2->finrl==0.3.6) (2023.7.1)\n",
            "Requirement already satisfied: referencing>=0.28.4 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from jsonschema->ray[default,tune]<3,>=2->finrl==0.3.6) (0.30.2)\n",
            "Requirement already satisfied: rpds-py>=0.7.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from jsonschema->ray[default,tune]<3,>=2->finrl==0.3.6) (0.10.6)\n",
            "Requirement already satisfied: opencensus-context>=0.1.3 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from opencensus->ray[default,tune]<3,>=2->finrl==0.3.6) (0.1.3)\n",
            "Requirement already satisfied: google-api-core<3.0.0,>=1.0.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from opencensus->ray[default,tune]<3,>=2->finrl==0.3.6) (2.12.0)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from rich->stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (3.0.0)\n",
            "Requirement already satisfied: importlib-resources in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from ale-py~=0.8.1->shimmy[atari]~=1.1.0->stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (6.1.0)\n",
            "Requirement already satisfied: wcwidth>=0.1.4 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from blessed>=1.17.1->gpustat>=1.0.0->ray[default,tune]<3,>=2->finrl==0.3.6) (0.2.8)\n",
            "Requirement already satisfied: pycparser in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from cffi>=1.12->cryptography>=2.6.1->ccxt<4,>=3->finrl==0.3.6) (2.21)\n",
            "Requirement already satisfied: googleapis-common-protos<2.0.dev0,>=1.56.2 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from google-api-core<3.0.0,>=1.0.0->opencensus->ray[default,tune]<3,>=2->finrl==0.3.6) (1.61.0)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from google-auth<3,>=1.6.3->tensorboard>=2.9.1->stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (5.3.1)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from google-auth<3,>=1.6.3->tensorboard>=2.9.1->stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (0.3.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from google-auth<3,>=1.6.3->tensorboard>=2.9.1->stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard>=2.9.1->stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (1.3.1)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from jedi>=0.16->ipython>=3.2.3->pyfolio<0.10,>=0.9->finrl==0.3.6) (0.8.3)\n",
            "Requirement already satisfied: mdurl~=0.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich->stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (0.1.2)\n",
            "Requirement already satisfied: qdldl in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from osqp>=0.6.2->cvxpy<2.0.0,>=1.1.19->pyportfolioopt<2,>=1->finrl==0.3.6) (0.1.7.post0)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from pexpect>4.3->ipython>=3.2.3->pyfolio<0.10,>=0.9->finrl==0.3.6) (0.7.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from werkzeug>=1.0.1->tensorboard>=2.9.1->stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (2.1.3)\n",
            "Requirement already satisfied: executing>=1.2.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from stack-data->ipython>=3.2.3->pyfolio<0.10,>=0.9->finrl==0.3.6) (1.2.0)\n",
            "Requirement already satisfied: asttokens>=2.1.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from stack-data->ipython>=3.2.3->pyfolio<0.10,>=0.9->finrl==0.3.6) (2.4.0)\n",
            "Requirement already satisfied: pure-eval in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from stack-data->ipython>=3.2.3->pyfolio<0.10,>=0.9->finrl==0.3.6) (0.2.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from sympy->torch>=1.13->stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (1.3.0)\n",
            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard>=2.9.1->stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (0.5.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /opt/homebrew/Caskroom/miniconda/base/envs/finRL/lib/python3.10/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard>=2.9.1->stable-baselines3[extra]>=2.0.0a5->finrl==0.3.6) (3.2.2)\n"
          ]
        }
      ],
      "source": [
        "## install required packages\n",
        "!pip install swig\n",
        "!pip install wrds\n",
        "!pip install pyportfolioopt\n",
        "## install finrl library\n",
        "!pip install git+https://github.com/AI4Finance-Foundation/FinRL.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "mqfBOKz-qJYF"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from finrl.meta.preprocessor.yahoodownloader import YahooDownloader\n",
        "from finrl.meta.env_stock_trading.env_stocktrading import StockTradingEnv\n",
        "from finrl.agents.stablebaselines3.models import DRLAgent\n",
        "from stable_baselines3 import A2C, DDPG, PPO, SAC, TD3\n",
        "\n",
        "%matplotlib inline\n",
        "from finrl.config import INDICATORS, TRAINED_MODEL_DIR"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kyv8fz5rqM7H"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mUF2P4hmqVjh"
      },
      "source": [
        "# Part 2. Backtesting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BdU6qLsVWDxI"
      },
      "source": [
        "To backtest the agents, upload trade_data.csv in the same directory of this notebook. For Colab users, just upload trade_data.csv to the default directory."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "mSjBHn_MZr4U"
      },
      "outputs": [],
      "source": [
        "train = pd.read_csv('train_data.csv')\n",
        "trade = pd.read_csv('trade_data.csv')\n",
        "\n",
        "# If you are not using the data generated from part 1 of this tutorial, make sure \n",
        "# it has the columns and index in the form that could be make into the environment. \n",
        "# Then you can comment and skip the following lines.\n",
        "train = train.set_index(train.columns[0])\n",
        "train.index.names = ['']\n",
        "trade = trade.set_index(trade.columns[0])\n",
        "trade.index.names = ['']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qu4Ey54b36oL"
      },
      "source": [
        "Then, upload the trained agent to the same directory, and set the corresponding variable to True."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "Z_mVZM4IIa55"
      },
      "outputs": [],
      "source": [
        "if_using_a2c = True\n",
        "if_using_ddpg = True\n",
        "if_using_ppo = True\n",
        "if_using_td3 = True\n",
        "if_using_sac = True"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "73D4oRqAIkYj"
      },
      "source": [
        "Load the agents"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "6CagrX0I36ZN"
      },
      "outputs": [],
      "source": [
        "trained_a2c = A2C.load(TRAINED_MODEL_DIR + \"/agent_a2c\") if if_using_a2c else None\n",
        "trained_ddpg = DDPG.load(TRAINED_MODEL_DIR + \"/agent_ddpg\") if if_using_ddpg else None\n",
        "trained_ppo = PPO.load(TRAINED_MODEL_DIR + \"/agent_ppo\") if if_using_ppo else None\n",
        "trained_td3 = TD3.load(TRAINED_MODEL_DIR + \"/agent_td3\") if if_using_td3 else None\n",
        "trained_sac = SAC.load(TRAINED_MODEL_DIR + \"/agent_sac\") if if_using_sac else None"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U5mmgQF_h1jQ"
      },
      "source": [
        "### Trading (Out-of-sample Performance)\n",
        "\n",
        "We update periodically in order to take full advantage of the data, e.g., retrain quarterly, monthly or weekly. We also tune the parameters along the way, in this notebook we use the in-sample data from 2009-01 to 2020-07 to tune the parameters once, so there is some alpha decay here as the length of trade date extends. \n",
        "\n",
        "Numerous hyperparameters – e.g. the learning rate, the total number of samples to train on – influence the learning process and are usually determined by testing some variations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4H_w3SaBAkKU",
        "outputId": "fdaed3a7-d3a9-4cde-d194-ee4576057175"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Stock Dimension: 29, State Space: 291\n"
          ]
        }
      ],
      "source": [
        "stock_dimension = len(trade.tic.unique())\n",
        "state_space = 1 + 2*stock_dimension + len(INDICATORS)*stock_dimension\n",
        "print(f\"Stock Dimension: {stock_dimension}, State Space: {state_space}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "nKNmQMqGAknW"
      },
      "outputs": [],
      "source": [
        "buy_cost_list = sell_cost_list = [0.001] * stock_dimension\n",
        "num_stock_shares = [0] * stock_dimension\n",
        "\n",
        "env_kwargs = {\n",
        "    \"hmax\": 100,\n",
        "    \"initial_amount\": 1000000,\n",
        "    \"num_stock_shares\": num_stock_shares,\n",
        "    \"buy_cost_pct\": buy_cost_list,\n",
        "    \"sell_cost_pct\": sell_cost_list,\n",
        "    \"state_space\": state_space,\n",
        "    \"stock_dim\": stock_dimension,\n",
        "    \"tech_indicator_list\": INDICATORS,\n",
        "    \"action_space\": stock_dimension,\n",
        "    \"reward_scaling\": 1e-4\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "cIqoV0GSI52v"
      },
      "outputs": [],
      "source": [
        "e_trade_gym = StockTradingEnv(df = trade, turbulence_threshold = 70,risk_indicator_col='vix', **env_kwargs)\n",
        "env_trade, obs_trade = e_trade_gym.get_sb_env()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lbFchno5j3xs",
        "outputId": "44fffa47-3b47-4e7b-96c2-0a485e9efead"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "hit end!\n"
          ]
        }
      ],
      "source": [
        "df_account_value_a2c, df_actions_a2c = DRLAgent.DRL_prediction(\n",
        "    model=trained_a2c, \n",
        "    environment = e_trade_gym) if if_using_a2c else (None, None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "JbYljWGjj3pH"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "hit end!\n"
          ]
        }
      ],
      "source": [
        "df_account_value_ddpg, df_actions_ddpg = DRLAgent.DRL_prediction(\n",
        "    model=trained_ddpg, \n",
        "    environment = e_trade_gym) if if_using_ddpg else (None, None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "74jNP2DBj3hb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "hit end!\n"
          ]
        }
      ],
      "source": [
        "df_account_value_ppo, df_actions_ppo = DRLAgent.DRL_prediction(\n",
        "    model=trained_ppo, \n",
        "    environment = e_trade_gym) if if_using_ppo else (None, None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "S7VyGGJPj3SH"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "hit end!\n"
          ]
        }
      ],
      "source": [
        "df_account_value_td3, df_actions_td3 = DRLAgent.DRL_prediction(\n",
        "    model=trained_td3, \n",
        "    environment = e_trade_gym) if if_using_td3 else (None, None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eLOnL5eYh1jR",
        "outputId": "70e50e24-aed5-49f9-cdd7-de6b9689d9ce"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "hit end!\n"
          ]
        }
      ],
      "source": [
        "df_account_value_sac, df_actions_sac = DRLAgent.DRL_prediction(\n",
        "    model=trained_sac, \n",
        "    environment = e_trade_gym) if if_using_sac else (None, None)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GcE-t08w6DaW"
      },
      "source": [
        "# Part 3: Mean Variance Optimization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "17TUs71EWj09"
      },
      "source": [
        "Mean Variance optimization is a very classic strategy in portfolio management. Here, we go through the whole process to do the mean variance optimization and add it as a baseline to compare.\n",
        "\n",
        "First, process dataframe to the form for MVO weight calculation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "wungSNOwPwKR"
      },
      "outputs": [],
      "source": [
        "def process_df_for_mvo(df):\n",
        "  df = df.sort_values(['date','tic'],ignore_index=True)[['date','tic','close']]\n",
        "  fst = df\n",
        "  fst = fst.iloc[0:stock_dimension, :]\n",
        "  tic = fst['tic'].tolist()\n",
        "\n",
        "  mvo = pd.DataFrame()\n",
        "\n",
        "  for k in range(len(tic)):\n",
        "    mvo[tic[k]] = 0\n",
        "\n",
        "  for i in range(df.shape[0]//stock_dimension):\n",
        "    n = df\n",
        "    n = n.iloc[i * stock_dimension:(i+1) * stock_dimension, :]\n",
        "    date = n['date'][i*stock_dimension]\n",
        "    mvo.loc[date] = n['close'].tolist()\n",
        "  \n",
        "  return mvo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SwEwkHJ1d_6u"
      },
      "source": [
        "### Helper functions for mean returns and variance-covariance matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "6KvXkpyE8MFq"
      },
      "outputs": [],
      "source": [
        "# Codes in this section partially refer to Dr G A Vijayalakshmi Pai\n",
        "\n",
        "# https://www.kaggle.com/code/vijipai/lesson-5-mean-variance-optimization-of-portfolios/notebook\n",
        "\n",
        "def StockReturnsComputing(StockPrice, Rows, Columns): \n",
        "  import numpy as np \n",
        "  StockReturn = np.zeros([Rows-1, Columns]) \n",
        "  for j in range(Columns):        # j: Assets \n",
        "    for i in range(Rows-1):     # i: Daily Prices \n",
        "      StockReturn[i,j]=((StockPrice[i+1, j]-StockPrice[i,j])/StockPrice[i,j])* 100 \n",
        "      \n",
        "  return StockReturn"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IeVVbuwveJ_5"
      },
      "source": [
        "### Calculate the weights for mean-variance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kE8nruKLQYLO",
        "outputId": "42d07c80-f309-49f8-f2b4-36a51987086f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[ 89.25041962, 230.55337524,  90.06215668, ...,  45.19828033,\n",
              "         35.11460114, 113.78933716],\n",
              "       [ 89.25041962, 233.37295532,  90.33027649, ...,  45.29749298,\n",
              "         36.05947113, 113.33300781],\n",
              "       [ 91.63790131, 231.57455444,  92.48486328, ...,  45.66953659,\n",
              "         37.07305145, 113.02877808],\n",
              "       ...,\n",
              "       [146.9400177 , 194.42747498, 177.35267639, ...,  46.44018173,\n",
              "         44.49522781, 145.47285461],\n",
              "       [147.61225891, 195.41072083, 176.02963257, ...,  46.88179779,\n",
              "         43.67258072, 144.20289612],\n",
              "       [147.14762878, 193.44422913, 173.1890564 , ...,  46.48434067,\n",
              "         42.59680557, 143.02020264]])"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "StockData = process_df_for_mvo(train)\n",
        "TradeData = process_df_for_mvo(trade)\n",
        "\n",
        "TradeData.to_numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u6_O6vrn_uD4",
        "outputId": "0c2f8bf7-07e7-4fe5-c409-93312b95a8dd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean returns of assets in k-portfolio 1\n",
            " [0.136 0.068 0.086 0.083 0.066 0.134 0.06  0.035 0.072 0.056 0.103 0.073\n",
            " 0.033 0.076 0.047 0.073 0.042 0.056 0.054 0.056 0.103 0.089 0.041 0.053\n",
            " 0.104 0.11  0.044 0.042 0.042]\n",
            "Variance-Covariance matrix of returns\n",
            " [[3.156 1.066 1.768 1.669 1.722 1.814 1.569 1.302 1.302 1.811 1.303 1.432\n",
            "  1.218 1.674 0.74  1.839 0.719 0.884 1.241 0.823 1.561 1.324 0.752 1.027\n",
            "  1.298 1.466 0.657 1.078 0.631]\n",
            " [1.066 2.571 1.306 1.123 1.193 1.319 1.116 1.053 1.045 1.269 1.068 1.089\n",
            "  0.899 1.218 0.926 1.391 0.682 0.727 1.025 1.156 1.166 0.984 0.798 0.956\n",
            "  1.259 1.111 0.688 1.091 0.682]\n",
            " [1.768 1.306 4.847 2.73  2.6   2.128 1.944 2.141 2.17  3.142 1.932 2.283\n",
            "  1.56  2.012 0.993 3.707 1.094 1.319 1.845 1.236 1.899 1.894 1.041 1.921\n",
            "  1.823 2.314 0.986 1.421 0.707]\n",
            " [1.669 1.123 2.73  4.892 2.363 1.979 1.7   2.115 1.959 2.387 1.773 2.319\n",
            "  1.571 1.797 0.968 2.597 1.144 1.298 1.643 1.071 1.615 1.775 0.91  1.666\n",
            "  1.707 1.784 0.82  1.345 0.647]\n",
            " [1.722 1.193 2.6   2.363 4.019 2.127 1.917 2.059 1.817 2.46  1.577 2.238\n",
            "  1.513 1.929 0.925 2.64  0.947 0.971 1.894 1.089 1.711 1.642 0.865 1.456\n",
            "  1.478 1.687 0.92  1.326 0.697]\n",
            " [1.814 1.319 2.128 1.979 2.127 5.384 1.974 1.549 1.683 2.122 1.624 1.771\n",
            "  1.441 1.939 0.846 2.191 0.837 1.075 1.475 1.041 1.978 1.768 0.784 1.328\n",
            "  1.365 1.912 0.787 1.28  0.666]\n",
            " [1.569 1.116 1.944 1.7   1.917 1.974 3.081 1.483 1.534 1.937 1.367 1.62\n",
            "  1.399 1.843 0.894 2.057 0.794 0.905 1.438 1.014 1.72  1.382 0.865 1.206\n",
            "  1.273 1.488 0.811 1.173 0.753]\n",
            " [1.302 1.053 2.141 2.115 2.059 1.549 1.483 2.842 1.525 2.044 1.428 1.783\n",
            "  1.308 1.533 0.878 2.279 0.938 1.092 1.385 1.078 1.429 1.314 0.831 1.459\n",
            "  1.466 1.48  0.83  1.042 0.567]\n",
            " [1.302 1.045 2.17  1.959 1.817 1.683 1.534 1.525 2.661 1.987 1.454 1.748\n",
            "  1.217 1.475 0.791 2.216 0.896 0.973 1.396 0.949 1.379 1.407 0.859 1.268\n",
            "  1.281 1.454 0.81  1.143 0.667]\n",
            " [1.811 1.269 3.142 2.387 2.46  2.122 1.937 2.044 1.987 4.407 1.789 2.12\n",
            "  1.593 1.982 0.945 3.96  0.956 1.094 1.758 1.157 1.788 1.692 0.905 1.879\n",
            "  1.712 2.    0.945 1.421 0.713]\n",
            " [1.303 1.068 1.932 1.773 1.577 1.624 1.367 1.428 1.454 1.789 2.373 1.51\n",
            "  1.166 1.501 0.756 1.941 0.824 0.998 1.239 0.887 1.366 1.414 0.797 1.299\n",
            "  1.296 1.41  0.764 1.071 0.783]\n",
            " [1.432 1.089 2.283 2.319 2.238 1.771 1.62  1.783 1.748 2.12  1.51  2.516\n",
            "  1.326 1.575 0.889 2.345 0.958 1.022 1.623 1.02  1.489 1.532 0.848 1.377\n",
            "  1.444 1.547 0.81  1.211 0.63 ]\n",
            " [1.218 0.899 1.56  1.571 1.513 1.441 1.399 1.308 1.217 1.593 1.166 1.326\n",
            "  2.052 1.399 0.727 1.749 0.786 0.795 1.154 0.829 1.296 1.12  0.743 1.105\n",
            "  1.088 1.214 0.739 0.998 0.598]\n",
            " [1.674 1.218 2.012 1.797 1.929 1.939 1.843 1.533 1.475 1.982 1.501 1.575\n",
            "  1.399 3.289 0.853 2.112 0.85  0.89  1.412 1.002 1.9   1.352 0.842 1.317\n",
            "  1.334 1.487 0.847 1.165 0.766]\n",
            " [0.74  0.926 0.993 0.968 0.925 0.846 0.894 0.878 0.791 0.945 0.756 0.889\n",
            "  0.727 0.853 1.153 1.027 0.642 0.59  0.848 0.892 0.825 0.748 0.694 0.761\n",
            "  0.929 0.819 0.61  0.806 0.547]\n",
            " [1.839 1.391 3.707 2.597 2.64  2.191 2.057 2.279 2.216 3.96  1.941 2.345\n",
            "  1.749 2.112 1.027 5.271 1.08  1.235 1.892 1.297 1.91  1.85  1.068 2.164\n",
            "  1.85  2.169 1.112 1.555 0.779]\n",
            " [0.719 0.682 1.094 1.144 0.947 0.837 0.794 0.938 0.896 0.956 0.824 0.958\n",
            "  0.786 0.85  0.642 1.08  1.264 0.679 0.804 0.74  0.819 0.845 0.749 0.891\n",
            "  0.849 0.794 0.633 0.719 0.514]\n",
            " [0.884 0.727 1.319 1.298 0.971 1.075 0.905 1.092 0.973 1.094 0.998 1.022\n",
            "  0.795 0.89  0.59  1.235 0.679 1.518 0.816 0.719 0.943 1.027 0.615 1.\n",
            "  0.947 0.994 0.533 0.673 0.504]\n",
            " [1.241 1.025 1.845 1.643 1.894 1.475 1.438 1.385 1.396 1.758 1.239 1.623\n",
            "  1.154 1.412 0.848 1.892 0.804 0.816 2.028 0.9   1.265 1.243 0.787 1.194\n",
            "  1.193 1.282 0.752 1.099 0.622]\n",
            " [0.823 1.156 1.236 1.071 1.089 1.041 1.014 1.078 0.949 1.157 0.887 1.02\n",
            "  0.829 1.002 0.892 1.297 0.74  0.719 0.9   2.007 0.952 0.849 0.732 1.008\n",
            "  1.15  0.933 0.722 0.897 0.614]\n",
            " [1.561 1.166 1.899 1.615 1.711 1.978 1.72  1.429 1.379 1.788 1.366 1.489\n",
            "  1.296 1.9   0.825 1.91  0.819 0.943 1.265 0.952 2.759 1.308 0.832 1.214\n",
            "  1.285 1.493 0.793 1.113 0.705]\n",
            " [1.324 0.984 1.894 1.775 1.642 1.768 1.382 1.314 1.407 1.692 1.414 1.532\n",
            "  1.12  1.352 0.748 1.85  0.845 1.027 1.243 0.849 1.308 2.864 0.751 1.153\n",
            "  1.26  1.411 0.71  1.046 0.651]\n",
            " [0.752 0.798 1.041 0.91  0.865 0.784 0.865 0.831 0.859 0.905 0.797 0.848\n",
            "  0.743 0.842 0.694 1.068 0.749 0.615 0.787 0.732 0.832 0.751 1.289 0.806\n",
            "  0.766 0.763 0.663 0.797 0.645]\n",
            " [1.027 0.956 1.921 1.666 1.456 1.328 1.206 1.459 1.268 1.879 1.299 1.377\n",
            "  1.105 1.317 0.761 2.164 0.891 1.    1.194 1.008 1.214 1.153 0.806 2.27\n",
            "  1.259 1.294 0.812 0.986 0.676]\n",
            " [1.298 1.259 1.823 1.707 1.478 1.365 1.273 1.466 1.281 1.712 1.296 1.444\n",
            "  1.088 1.334 0.929 1.85  0.849 0.947 1.193 1.15  1.285 1.26  0.766 1.259\n",
            "  3.352 1.267 0.697 1.137 0.685]\n",
            " [1.466 1.111 2.314 1.784 1.687 1.912 1.488 1.48  1.454 2.    1.41  1.547\n",
            "  1.214 1.487 0.819 2.169 0.794 0.994 1.282 0.933 1.493 1.411 0.763 1.294\n",
            "  1.267 2.982 0.709 1.007 0.656]\n",
            " [0.657 0.688 0.986 0.82  0.92  0.787 0.811 0.83  0.81  0.945 0.764 0.81\n",
            "  0.739 0.847 0.61  1.112 0.633 0.533 0.752 0.722 0.793 0.71  0.663 0.812\n",
            "  0.697 0.709 1.371 0.697 0.561]\n",
            " [1.078 1.091 1.421 1.345 1.326 1.28  1.173 1.042 1.143 1.421 1.071 1.211\n",
            "  0.998 1.165 0.806 1.555 0.719 0.673 1.099 0.897 1.113 1.046 0.797 0.986\n",
            "  1.137 1.007 0.697 3.073 0.759]\n",
            " [0.631 0.682 0.707 0.647 0.697 0.666 0.753 0.567 0.667 0.713 0.783 0.63\n",
            "  0.598 0.766 0.547 0.779 0.514 0.504 0.622 0.614 0.705 0.651 0.645 0.676\n",
            "  0.685 0.656 0.561 0.759 1.452]]\n"
          ]
        }
      ],
      "source": [
        "#compute asset returns\n",
        "arStockPrices = np.asarray(StockData)\n",
        "[Rows, Cols]=arStockPrices.shape\n",
        "arReturns = StockReturnsComputing(arStockPrices, Rows, Cols)\n",
        "\n",
        "#compute mean returns and variance covariance matrix of returns\n",
        "meanReturns = np.mean(arReturns, axis = 0)\n",
        "covReturns = np.cov(arReturns, rowvar=False)\n",
        " \n",
        "#set precision for printing results\n",
        "np.set_printoptions(precision=3, suppress = True)\n",
        "\n",
        "#display mean returns and variance-covariance matrix of returns\n",
        "print('Mean returns of assets in k-portfolio 1\\n', meanReturns)\n",
        "print('Variance-Covariance matrix of returns\\n', covReturns)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zC7r-cI8RR1X"
      },
      "source": [
        "### Use PyPortfolioOpt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b1btTONEdCU4",
        "outputId": "75096462-7dfb-4ce6-c6f4-4671f11e79fc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([424250.,      0.,      0.,      0.,      0., 108650.,      0.,\n",
              "            0.,      0.,      0., 181450.,      0.,      0.,      0.,\n",
              "            0.,      0.,      0.,      0.,      0.,      0.,  16960.,\n",
              "            0.,      0.,      0., 133540., 135150.,      0.,      0.,\n",
              "            0.])"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from pypfopt.efficient_frontier import EfficientFrontier\n",
        "\n",
        "ef_mean = EfficientFrontier(meanReturns, covReturns, weight_bounds=(0, 0.5))\n",
        "raw_weights_mean = ef_mean.max_sharpe()\n",
        "cleaned_weights_mean = ef_mean.clean_weights()\n",
        "mvo_weights = np.array([1000000 * cleaned_weights_mean[i] for i in range(29)])\n",
        "mvo_weights"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F38NJRJJgOmj",
        "outputId": "f575651b-1e9b-4015-ae71-c9fc2c3a3dae"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([4744.487,    0.   ,    0.   ,    0.   ,    0.   ,  579.993,\n",
              "          0.   ,    0.   ,    0.   ,    0.   ,  782.279,    0.   ,\n",
              "          0.   ,    0.   ,    0.   ,    0.   ,    0.   ,    0.   ,\n",
              "          0.   ,    0.   ,   85.833,    0.   ,    0.   ,    0.   ,\n",
              "        474.034,  715.534,    0.   ,    0.   ,    0.   ])"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "LastPrice = np.array([1/p for p in StockData.tail(1).to_numpy()[0]])\n",
        "Initial_Portfolio = np.multiply(mvo_weights, LastPrice)\n",
        "Initial_Portfolio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "ZAd1iXqZhQ6X"
      },
      "outputs": [],
      "source": [
        "Portfolio_Assets = TradeData @ Initial_Portfolio\n",
        "MVO_result = pd.DataFrame(Portfolio_Assets, columns=[\"Mean Var\"])\n",
        "# MVO_result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I5sgGe7g1HsL"
      },
      "source": [
        "# Part 4: DJIA index"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sVe_ufxTY2CW"
      },
      "source": [
        "Add DJIA index as a baseline to compare with."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "sACPzsI-6k8q"
      },
      "outputs": [],
      "source": [
        "TRAIN_START_DATE = '2009-01-01'\n",
        "TRAIN_END_DATE = '2020-07-01'\n",
        "TRADE_START_DATE = '2020-07-01'\n",
        "TRADE_END_DATE = '2021-10-29'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TuszW-OB1K0m",
        "outputId": "b89a8350-de58-4fea-8e4b-856efa872712"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (319, 8)\n"
          ]
        }
      ],
      "source": [
        "df_dji = YahooDownloader(start_date = TRADE_START_DATE,\n",
        "                     end_date = TRADE_END_DATE,\n",
        "                     ticker_list = ['dji']).fetch_data()\n",
        "# df_dji"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "Q3RXz72U1VbV"
      },
      "outputs": [],
      "source": [
        "df_dji = df_dji[['date','close']]\n",
        "fst_day = df_dji['close'][0]\n",
        "dji = pd.merge(df_dji['date'], df_dji['close'].div(fst_day).mul(1000000), \n",
        "               how='outer', left_index=True, right_index=True).set_index('date')\n",
        "# dji"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6vvNSC6h1jZ"
      },
      "source": [
        "<a id='4'></a>\n",
        "# Part 5: Backtesting Results\n",
        "Backtesting plays a key role in evaluating the performance of a trading strategy. Automated backtesting tool is preferred because it reduces the human error. We usually use the Quantopian pyfolio package to backtest our trading strategies. It is easy to use and consists of various individual plots that provide a comprehensive image of the performance of a trading strategy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "KeDeGAc9VrEg"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/var/folders/pc/8l9dz1f949ddzztd4yfcytx80000gn/T/ipykernel_75154/3895181226.py:14: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
            "  result = pd.merge(result, dji, how='outer', left_index=True, right_index=True).fillna(method='bfill')\n"
          ]
        }
      ],
      "source": [
        "df_result_a2c = df_account_value_a2c.set_index(df_account_value_a2c.columns[0]) if if_using_a2c else None\n",
        "df_result_ddpg = df_account_value_ddpg.set_index(df_account_value_ddpg.columns[0]) if if_using_ddpg else None\n",
        "df_result_ppo = df_account_value_ppo.set_index(df_account_value_ppo.columns[0]) if if_using_ppo else None\n",
        "df_result_td3 = df_account_value_td3.set_index(df_account_value_td3.columns[0]) if if_using_td3 else None\n",
        "df_result_sac = df_account_value_sac.set_index(df_account_value_sac.columns[0]) if if_using_sac else None\n",
        "\n",
        "result = pd.DataFrame()\n",
        "if if_using_a2c: result = pd.merge(result, df_result_a2c, how='outer', left_index=True, right_index=True)\n",
        "if if_using_ddpg: result = pd.merge(result, df_result_ddpg, how='outer', left_index=True, right_index=True, suffixes=('', '_drop'))\n",
        "if if_using_ppo: result = pd.merge(result, df_result_ppo, how='outer', left_index=True, right_index=True, suffixes=('', '_drop'))\n",
        "if if_using_td3: result = pd.merge(result, df_result_td3, how='outer', left_index=True, right_index=True, suffixes=('', '_drop'))\n",
        "if if_using_sac: result = pd.merge(result, df_result_sac, how='outer', left_index=True, right_index=True, suffixes=('', '_drop'))\n",
        "result = pd.merge(result, MVO_result, how='outer', left_index=True, right_index=True)\n",
        "result = pd.merge(result, dji, how='outer', left_index=True, right_index=True).fillna(method='bfill')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "JvlTVQwsiMyx"
      },
      "outputs": [],
      "source": [
        "col_name = []\n",
        "col_name.append('A2C') if if_using_a2c else None\n",
        "col_name.append('DDPG') if if_using_ddpg else None\n",
        "col_name.append('PPO') if if_using_ppo else None\n",
        "col_name.append('TD3') if if_using_td3 else None\n",
        "col_name.append('SAC') if if_using_sac else None\n",
        "col_name.append('Mean Var')\n",
        "col_name.append('djia') \n",
        "result.columns = col_name"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "l4FZxyDt3XaE",
        "outputId": "2e739637-bf88-4698-9cf1-9a526452e465"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>A2C</th>\n",
              "      <th>DDPG</th>\n",
              "      <th>PPO</th>\n",
              "      <th>TD3</th>\n",
              "      <th>SAC</th>\n",
              "      <th>Mean Var</th>\n",
              "      <th>djia</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-07-01</th>\n",
              "      <td>1.000000e+06</td>\n",
              "      <td>1.000000e+06</td>\n",
              "      <td>1.000000e+06</td>\n",
              "      <td>1.000000e+06</td>\n",
              "      <td>1.000000e+06</td>\n",
              "      <td>1.001918e+06</td>\n",
              "      <td>1.000000e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-02</th>\n",
              "      <td>1.000796e+06</td>\n",
              "      <td>1.000881e+06</td>\n",
              "      <td>1.000164e+06</td>\n",
              "      <td>1.000382e+06</td>\n",
              "      <td>1.000484e+06</td>\n",
              "      <td>1.004235e+06</td>\n",
              "      <td>1.021449e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-06</th>\n",
              "      <td>1.004186e+06</td>\n",
              "      <td>1.007218e+06</td>\n",
              "      <td>1.000465e+06</td>\n",
              "      <td>1.008231e+06</td>\n",
              "      <td>1.004442e+06</td>\n",
              "      <td>1.023225e+06</td>\n",
              "      <td>1.021449e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-07</th>\n",
              "      <td>9.957335e+05</td>\n",
              "      <td>9.938203e+05</td>\n",
              "      <td>9.993283e+05</td>\n",
              "      <td>9.976369e+05</td>\n",
              "      <td>9.974599e+05</td>\n",
              "      <td>1.014021e+06</td>\n",
              "      <td>1.006031e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-08</th>\n",
              "      <td>9.983700e+05</td>\n",
              "      <td>9.988223e+05</td>\n",
              "      <td>9.995208e+05</td>\n",
              "      <td>1.001148e+06</td>\n",
              "      <td>9.983567e+05</td>\n",
              "      <td>1.029461e+06</td>\n",
              "      <td>1.012912e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-10-22</th>\n",
              "      <td>1.379239e+06</td>\n",
              "      <td>1.345651e+06</td>\n",
              "      <td>1.313267e+06</td>\n",
              "      <td>1.460297e+06</td>\n",
              "      <td>1.236512e+06</td>\n",
              "      <td>1.535668e+06</td>\n",
              "      <td>1.386322e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-10-25</th>\n",
              "      <td>1.382223e+06</td>\n",
              "      <td>1.350216e+06</td>\n",
              "      <td>1.320532e+06</td>\n",
              "      <td>1.462491e+06</td>\n",
              "      <td>1.236275e+06</td>\n",
              "      <td>1.542078e+06</td>\n",
              "      <td>1.388813e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-10-26</th>\n",
              "      <td>1.384209e+06</td>\n",
              "      <td>1.347201e+06</td>\n",
              "      <td>1.320763e+06</td>\n",
              "      <td>1.462833e+06</td>\n",
              "      <td>1.230254e+06</td>\n",
              "      <td>1.545514e+06</td>\n",
              "      <td>1.389427e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-10-27</th>\n",
              "      <td>1.367469e+06</td>\n",
              "      <td>1.334625e+06</td>\n",
              "      <td>1.318830e+06</td>\n",
              "      <td>1.452726e+06</td>\n",
              "      <td>1.216500e+06</td>\n",
              "      <td>1.534916e+06</td>\n",
              "      <td>1.379083e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-10-28</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.388401e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>336 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                     A2C          DDPG           PPO           TD3  \\\n",
              "date                                                                 \n",
              "2020-07-01  1.000000e+06  1.000000e+06  1.000000e+06  1.000000e+06   \n",
              "2020-07-02  1.000796e+06  1.000881e+06  1.000164e+06  1.000382e+06   \n",
              "2020-07-06  1.004186e+06  1.007218e+06  1.000465e+06  1.008231e+06   \n",
              "2020-07-07  9.957335e+05  9.938203e+05  9.993283e+05  9.976369e+05   \n",
              "2020-07-08  9.983700e+05  9.988223e+05  9.995208e+05  1.001148e+06   \n",
              "...                  ...           ...           ...           ...   \n",
              "2021-10-22  1.379239e+06  1.345651e+06  1.313267e+06  1.460297e+06   \n",
              "2021-10-25  1.382223e+06  1.350216e+06  1.320532e+06  1.462491e+06   \n",
              "2021-10-26  1.384209e+06  1.347201e+06  1.320763e+06  1.462833e+06   \n",
              "2021-10-27  1.367469e+06  1.334625e+06  1.318830e+06  1.452726e+06   \n",
              "2021-10-28           NaN           NaN           NaN           NaN   \n",
              "\n",
              "                     SAC      Mean Var          djia  \n",
              "date                                                  \n",
              "2020-07-01  1.000000e+06  1.001918e+06  1.000000e+06  \n",
              "2020-07-02  1.000484e+06  1.004235e+06  1.021449e+06  \n",
              "2020-07-06  1.004442e+06  1.023225e+06  1.021449e+06  \n",
              "2020-07-07  9.974599e+05  1.014021e+06  1.006031e+06  \n",
              "2020-07-08  9.983567e+05  1.029461e+06  1.012912e+06  \n",
              "...                  ...           ...           ...  \n",
              "2021-10-22  1.236512e+06  1.535668e+06  1.386322e+06  \n",
              "2021-10-25  1.236275e+06  1.542078e+06  1.388813e+06  \n",
              "2021-10-26  1.230254e+06  1.545514e+06  1.389427e+06  \n",
              "2021-10-27  1.216500e+06  1.534916e+06  1.379083e+06  \n",
              "2021-10-28           NaN           NaN  1.388401e+06  \n",
              "\n",
              "[336 rows x 7 columns]"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QQuc5hI9Yklt"
      },
      "source": [
        "Now, everything is ready, we can plot the backtest result."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "6xRfrqK4RVfq",
        "outputId": "469c9729-fd57-417c-9b13-2243426923e2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='date'>"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.rcParams[\"figure.figsize\"] = (15,5)\n",
        "plt.figure()\n",
        "result.plot()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "GfZ5vY5wRjkJ"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.13"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
